U.S. patent application number 12/088845 was filed with the patent office on 2009-10-01 for systems and methods for tomographic image reconstruction.
This patent application is currently assigned to The Trustees of Dartmouth College. Invention is credited to Hamid Dehghani, Shudong Jiang, Keith D. Paulsen, Daqing Piao, Brian William Pogue, Roger Springett, Subhadra Srinivasan, Heng Xu.
Application Number | 20090247847 12/088845 |
Document ID | / |
Family ID | 37235393 |
Filed Date | 2009-10-01 |
United States Patent
Application |
20090247847 |
Kind Code |
A1 |
Pogue; Brian William ; et
al. |
October 1, 2009 |
SYSTEMS AND METHODS FOR TOMOGRAPHIC IMAGE RECONSTRUCTION
Abstract
Optical tomography systems that provide light of multiple
distinct wavelengths from a plurality of sources are described. The
systems direct light into mammalian tissue, and light from the
mammalian tissue is collected at a plurality of reception points.
Collected light from each reception point is separated according to
its wavelength, and received by a photodetector to produce path
attenuation signals representing attenuation along paths between
the source locations and the reception points. An image
construction system generates a tomographic image of the mammalian
tissue from the path attenuation signals. One embodiment of an
optical imaging system includes an optical coherence
tomography-near infrared probe. The systems and methods may utilize
a spectral derivative approach that provides insensitivity to the
boundary and boundary artifacts in the signal, thereby improving
the quality of the reconstructed images.
Inventors: |
Pogue; Brian William;
(Hanover, NH) ; Piao; Daqing; (Stillwater, OK)
; Paulsen; Keith D.; (Hanover, NH) ; Jiang;
Shudong; (Hanover, NH) ; Dehghani; Hamid;
(Exeter, GB) ; Xu; Heng; (Union City, CA) ;
Springett; Roger; (Hanover, NH) ; Srinivasan;
Subhadra; (Keene, NH) |
Correspondence
Address: |
LATHROP & GAGE LLP
4845 PEARL EAST CIRCLE, SUITE 201
BOULDER
CO
80301
US
|
Assignee: |
The Trustees of Dartmouth
College
Hanover
NH
|
Family ID: |
37235393 |
Appl. No.: |
12/088845 |
Filed: |
April 27, 2006 |
PCT Filed: |
April 27, 2006 |
PCT NO: |
PCT/US2006/016210 |
371 Date: |
April 8, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11115865 |
Apr 27, 2005 |
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12088845 |
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60689727 |
Jun 10, 2005 |
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Current U.S.
Class: |
600/323 ;
600/425 |
Current CPC
Class: |
A61B 5/4312 20130101;
A61B 5/0091 20130101; A61B 5/7239 20130101; A61B 5/0073 20130101;
A61B 5/14551 20130101 |
Class at
Publication: |
600/323 ;
600/425 |
International
Class: |
A61B 6/03 20060101
A61B006/03; A61B 5/1455 20060101 A61B005/1455 |
Goverment Interests
U.S. GOVERNMENT RIGHTS
[0002] This invention was made with Government support under grants
NIH RO1 NS39471, U54 CA105480, PO1CA80139, RO1CA69544 and
1R21CA100984-01A1 awarded by the National Institutes of Health, and
DAMD17-03-01-0405 awarded by the Department of Defense. The U.S.
Government has certain rights in this invention.
Claims
1. A tomography system, comprising: a plurality of lasers of a
first group, each laser of the first group for generating light of
a distinct wavelength within a first wavelength band; apparatus for
applying the light from lasers of the first group to mammalian
tissue at laser-specific locations; apparatus for collecting light
from the mammalian tissue at a plurality of reception points;
apparatus for separating light received from the apparatus for
collecting light according to a wavelength of the received light;
apparatus for generating a path attenuation signal encoding
received light amplitude information for each reception point at
each wavelength corresponding to each laser of the plurality of
lasers; and image construction apparatus for receiving the path
attenuation signal and for reconstructing a tomographic image of
the mammalian tissue.
2. The tomography system of claim 1, wherein the first wavelength
band is near infrared between 620 and 1000 nanometers.
3. The tomography system of claim 2, wherein the first wavelength
band is of bandwidth less than two percent of its center
wavelength.
4. The tomography system of claim 2, wherein the first wavelength
band is within the range 750 to 860 nanometers.
5. The tomography system of claim 3, wherein the apparatus for
generating a path attenuation signal further comprises a
diffraction grating and a charge-coupled device (CCD) image
sensor.
6. The tomography system of claim 3, wherein the apparatus for
generating a path attenuation signal further comprises a
diffraction grating, an image intensifier tube, and a
charge-coupled device (CCD) image sensor.
7. The tomography system of claim 3, wherein the apparatus for
generating a path attenuation signal further comprises a
diffraction grating, a fiber-optic distributor, and a plurality of
photomultiplier tubes.
8. The tomography system of claim 1, further comprising a plurality
of lasers of a second group, each laser of the second group for
generating light of a distinct wavelength within a second
wavelength band not overlapping the first wavelength band, and
wherein the image construction apparatus is capable of generating
an image corresponding to oxygenation of the mammalian tissue.
9. The tomography system of claim 8, wherein the first wavelength
band is of bandwidth less than one percent, and a center wavelength
of the first wavelength band is separated from a center wavelength
of the second wavelength band by between five and ten percent.
10. The tomography system of claim 9, wherein the first wavelength
band is within the range 750 to 860 nanometers and wherein the
apparatus for generating a path attenuation signal further
comprises a diffraction grating and a charge-coupled device (CCD)
image sensor.
11. The tomography system of claim 10, wherein the apparatus for
generating a path attenuation signal further comprises an image
intensifier tube for amplifying light separated according to
wavelength by the diffraction grating and for providing amplified
light to the CCD image sensor.
12. The tomography system of claim 9, wherein the apparatus for
generating a path attenuation signal further comprises a
diffraction grating, a fiber-optic distributor, and a plurality of
photomultiplier tubes.
13. A method of generating tomographic images of mammalian tissue
comprising: generating infrared light of a plurality of
laser-specific wavelengths in a first narrow band of wavelengths;
applying the infrared light to a plurality of laser-specific
locations on the mammalian tissue; receiving infrared light from a
plurality of reception points on the mammalian tissue; separating
received light from each reception point of the plurality of
reception points according to wavelength into separated received
light; transducing the separated received light into electronic
signals; and constructing a tomographic image of attenuation in the
mammalian tissue from the electronic signals.
14. The method of claim 13, wherein the first narrow band of
wavelengths is less than two percent wide, and is in the near
infrared between 620 and 1000 nanometers.
15. The method of claim 14, further comprising generating infrared
light of a plurality of laser-specific wavelengths in a second
narrow band of wavelengths, and wherein the first narrow band of
wavelengths is of width less than two percent.
16. The method of claim 15, wherein the second narrow band of
wavelengths has a center wavelength differing from a center
wavelength of the first narrow band of wavelengths by between five
and fifteen percent.
17. A software product comprising instructions, stored on
computer-readable media, wherein the instructions, when executed by
a computer, perform steps for creating a tomographic image of
tissue, comprising: instructions for obtaining data from a detector
indicative of light intensity; instructions for determining a ratio
of the light intensity for multiple source-detector pairs;
instructions for using the ratio data to reconstruct structural and
functional data of the tissue; and instructions for creating a
tomographic image of the tissue.
18. A software product comprising instructions, stored on
computer-readable media, wherein the instructions, when executed by
a computer, perform steps for creating a tomographic image of
tissue, comprising: instructions for obtaining data from a detector
indicative of light intensity; instructions for determining a ratio
of the light intensity for multiple source-detector pairs;
instructions for determining emission source concentration; and
instructions for creating a tomographic image of the tissue.
19. A method of creating an image of living mammalian tissue
comprising: collecting spectral intensity data from multiple
source-detector pairs; using Spectral Derivative Image
Reconstruction (SDIR) to manipulate the data; and using the SDIR
manipulated data in an image reconstruction model to obtain a
reconstructed image of the tissue.
20. The method of claim 19, further comprising evaluating the
reconstructed image to make a diagnosis of damage or disease to the
tissue.
21. A software product comprising instructions, stored on
computer-readable media, wherein the instructions, when executed by
a computer, perform steps for creating a tomographic image of
tissue, comprising: instructions for obtaining frequency-domain
data from a detector indicative of transmitted light intensity;
instructions for using the light intensity for multiple wavelengths
at one or more source-detector pairs in a spectrally-constrained
algorithm to reconstruct structural and functional data of the
tissue; and instructions for creating a tomographic image of the
tissue.
22. The software product of claim 17 or 21, further comprising
instructions for determining emission source concentration.
23. The software product of claim 17 or 21, wherein the
instructions for reconstructing functional data provide for
recovery of hemoglobin, oxyhemoglobin, water and lipid
concentrations.
24. The software product of claim 17 or 21, wherein the
instructions for reconstructing structural data provide for
recovery of scattering amplitude and scattering power.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. application Ser.
No. 11/115,865, filed Apr. 27, 2005, and to U.S. Application No.
60/689,727, filed Jun. 10, 2005, each of which is incorporated
herein by reference.
BACKGROUND
[0003] Diffuse Optical Tomography (DOT) is a technique wherein
tissue is illuminated at multiple source points on a tissue surface
with electromagnetic energy having wavelengths ranging from visible
light to near infrared (NIR). Light transmitted through the tissue
from each source point is then detected at each of multiple
reception points on the tissue surface to measure attenuation and
scattering along paths from each source point to each reception
point. Scattering is mainly the result of light interactions with
solid or semi-solid masses, whereas attenuation of the radiation
over the pathlength may be caused by absorption and/or emission.
For example, light is absorbed by compounds (chromophores) within
the tissue, such as hemoglobin, myoglobin, lipids and water, that
interact with electromagnetic energy of a particular wavelength.
Emission--radiation of energy from a molecule--can result from
naturally occurring fluorescent and bioluminescent molecules and/or
from medical imaging compositions in the tissue.
[0004] Measuring and modeling of attenuation and scattering allows
for the creation of potentially high contrast images. For example,
the heme group of myoglobin and/or hemoglobin absorbs visible and
near infrared radiation, and the spectral characteristics of the
absorption vary noticeably with the degree of oxygenation.
Therefore, high contrast may be obtained between portions of the
tissue containing high concentrations of heme (such as blood and
muscle) and portions of tissue containing low concentrations of
heme (such as fat), and between highly oxygenated and poorly
oxygenated or infarcted tissues. In particular, the high
vascularity in tumors provides an elevated hemoglobin content and a
potentially high intrinsic optical contrast between the tumor and
normal tissue.
[0005] Modeling of the tissue is typically performed with a
computerized tissue model having parameters that are adjusted such
that modeled tissue matches the measured attenuation and scattering
along each path. In some models, chromophore concentrations and
scatter parameters are determined by comparing absolute
transmission data to known (signature) spectra. Such systems are
subject to large noise contributions and errors, such as variations
between source and detector coupling coefficients, boundary
reflection mismatches, and inaccurate geometric modeling. These
errors arise because the systems attempt to match model-calculated
data with calibrated measurement data, which often contains these
coupling/boundary errors.
[0006] Spectrally-constrained models, such as Direct Chromophore
Spectral Reconstruction (DCSR), show improved accuracy and are more
robust in the presence of noise than conventional models, because
they use coupled spectral information to constrain the
reconstruction. DCSR is, however, subject to some of the same
measurement errors as traditional methods, namely, coupling
coefficient and external boundary variations, as well as inaccurate
geometric modeling. See, for example, Srinivasan, S.; Pogue, B. W.;
Jiang, S.; Dehghani, H.; Paulsen, K. D. "Spectrally Constrained
Chromophore and Scattering NIR Tomography Provides Quantitative and
Robust Reconstruction", Applied Optics, 44(10), 1858-1869, (2004)
and Corlu, A.; Durduran, T.; Choe, R.; Schweiger, M.; Hillman, E.
M. C.; Arridge, S. R.; Yodh, A. G. "Uniqueness and Wavelength
Optimization in Continuous-Wave Multispectral Diffuse Optical
Tomography", Optics Letters, 28(23), 2339-2341, (2003). The
parameterized tissue model is projected onto one or more
hypothetical image planes, which are prepared as two-dimensional
cross-sectional slices and/or three-dimensional images.
[0007] Systems for optical tomography, similar to that described in
C. H. Schmitz, M. Locker, J. M. Lasker, A. H. Hielscher, and R. L.
Barbour, "Instrumentation for fast functional optical tomography,"
Rev. Sci. Instr., 73(2): 429-439 (2002), have been marketed by NIRx
Medical Technologies, LLC of Glen Head, N.Y. The system marketed by
NIRx can resolve 5-millimeter lesions 3 centimeters below the skin
surface. The system of Schmitz mechanically distributes light from
a single laser into multiple illumination points spaced over the
tissue to be studied in succession. As each illumination point is
illuminated, light received at multiple reception points spaced
over the tissue is measured. With the apparatus of Schmitz, data
for approximately 3 image planes per second can be acquired.
[0008] The amount of heme at a particular soft-tissue location can,
however, vary rapidly, so that acquisition of data at a rate of 3
image planes per second may be insufficient to accurately detect a
physiological occurrence or anomaly. For example, both elastic and
muscular arteries, including associated pathology such as
aneurysms, may enlarge and shrink with each heartbeat. Active
muscle and brain tissue not only is known to consume oxygen at an
activity-dependent rate, thereby changing its spectral
characteristics, but it releases local vasoactive substances such
as adenosine with resulting activity-dependent vasodilation
occurring in seconds. Vasculature in different tissue types, such
as tumor and surrounding tissue, can also respond differently to
exogenous vasoactive substances. Similarly, since the corpora
cavernosa may undergo rapid changes in heme content and
oxygenation, imaging of those changes could be of interest in the
study, diagnosis and treatment of erectile dysfunction or
priapism.
[0009] The degree of oxygenation and heme content of soft tissue
regions under varying conditions can be of interest to a physician
attempting to diagnose disease. For example, it is known that many
malignant tumors require so much oxygen that portions of the tumor
may become ischemic and necrotic despite their increased
vascularity. Much heart disease is ischemic, as are many strokes.
Peripheral vascular disease, often implicated in diabetic foot
ulcers, often produces--sometimes activity-dependent--inadequate
blood flow and abnormal zones of ischemia in peripheral tissue such
as limb tissue. These zones of ischemia tend to be more prone to
forming slow or non-healing ulcers than normally oxygenated tissue.
Accurate imaging of vessel obstructions and ischemia in tissue may
allow for more successful debridement of ulcers and permit success
with other treatments such as revascularization. Imaging of rapid
activity-dependent changes in regional distribution of heme content
and oxygenation of brain tissue could be of interest in research
into brain function, as well as in the diagnosis of a wide variety
of neurological conditions including epilepsy.
[0010] It is desirable to have a short acquisition time to measure
the dynamic aspects of heme distribution, also known as
hemodynamics. It has been proposed that scattering and attenuation
for multiple paths can be acquired simultaneously using intensity
modulation encoding of the source. Franceschini (Francheschini et
al, "Frequency-domain techniques enhance optical mammography:
Initial clinical results" Proc Natl Acad Sci USA. 94(12):
6468-6473, 1997) demonstrated this approach with a frequency domain
source, and the concept was further developed by Siegel (Siegel, A
M, Marota, J J A and Boas, D A. "Design and evaluation of a
continuous-wave diffuse optical tomography system." Optics Express
4:287-298, 1999) for a continuous wave source based system. Siegel
developed a system where several source points are illuminated at
the same time. Light applied to each simultaneously-illuminated
source point is amplitude modulated such that light from that
source point can be distinguished from light applied to other
simultaneously-illuminated source points, by having a different
modulation frequency. For example, if one source point is
amplitude-modulated with a first tone, and a second source point is
amplitude-modulated with a second tone, light received at a
reception point can be distinguished by measuring a ratio between
the first and second tone in modulation as received at the
reception point.
SUMMARY
[0011] In one embodiment, a tomography system, includes: a
plurality of lasers of a first group, each laser of the first group
for generating light of a distinct wavelength within a first
wavelength band; apparatus for applying the light from lasers of
the first group to mammalian tissue at laser-specific locations;
apparatus for collecting light from the mammalian tissue at a
plurality of reception points; apparatus for separating light
received from the apparatus for collecting light according to a
wavelength of the received light; apparatus for generating a path
attenuation signal encoding received light amplitude information
for each reception point at each wavelength corresponding to each
laser of the plurality of lasers; and image construction apparatus
for receiving the path attenuation signal and for reconstructing a
tomographic image of the mammalian tissue.
[0012] In one embodiment, a method of generating tomographic images
of mammalian tissue includes: generating infrared light of a
plurality of laser-specific wavelengths in a first narrow band of
wavelengths; applying the infrared light to a plurality of
laser-specific locations on the mammalian tissue; receiving
infrared light from a plurality of reception points on the
mammalian tissue; separating received light from each reception
point of the plurality of reception points according to wavelength
into separated received light; transducing the separated received
light into electronic signals; and constructing a tomographic image
of attenuation in the mammalian tissue from the electronic
signals.
[0013] In one embodiment, a software product includes
computer-readable instructions, stored on computer-readable media,
wherein the instructions, when executed by a computer, perform
steps for creating a tomographic image of tissue. The instructions
include instructions for obtaining data from a detector indicative
of light intensity, instructions for determining the difference in
the ratio of intensity for multiple wavelengths at one or more
source-detector pairs, instructions for using the difference data
to reconstruct structural and functional data of the tissue, and
instructions for creating a tomographic image of the tissue.
Instructions for determining emission source concentration may also
be included.
[0014] In another embodiment, a software product includes
computer-readable instructions, stored on computer-readable media,
wherein the instructions, when executed by a computer, perform
steps for creating a tomographic image of tissue. The instructions
include instructions for obtaining data from a detector indicative
of light intensity, instructions for determining the difference in
the ratio of intensity for multiple wavelengths at one or more
source-detector pairs, instructions for determining emission source
concentration, and instructions for creating a tomographic image of
the tissue.
[0015] In another embodiment, a method of creating an image of
living mammalian tissue includes collecting spectral intensity data
from multiple source-detector pairs, using Spectral Derivative
Image Reconstruction (SDIR) to manipulate the data, and using the
SDIR manipulated data in an image reconstruction model to obtain a
reconstructed image of the tissue.
[0016] In another embodiment, a software product includes
computer-readable instructions, stored on computer-readable media,
wherein the instructions, when executed by a computer, perform
steps for creating a tomographic image of tissue. The instructions
include instructions for obtaining frequency-domain data from a
detector indicative of transmitted light intensity, instructions
for using the ratio of light intensity for multiple wavelengths at
one or more source-detector pairs in a spectrally-constrained
algorithm to reconstruct structural and functional data of the
tissue, and instructions for creating a tomographic image of the
tissue.
BRIEF DESCRIPTION OF DRAWINGS
[0017] FIG. 1 is a block diagram of a spectral-encoding system for
high-rate hemodynamic tomography, according to an embodiment.
[0018] FIG. 2 is a simplified block diagram of a near infrared
apparatus of FIG. 1.
[0019] FIG. 3 is a schematic image of light intensities viewed by a
CCD chip of the near infrared apparatus of FIG. 2.
[0020] FIG. 4 is a block diagram of a system for imaging oxygen
saturation, according to an embodiment.
[0021] FIG. 5 is a flowchart illustrating a method for
spectral-encoding for video-rate hemodynamic tomography, according
to an embodiment.
[0022] FIG. 6 is a block diagram of an embodiment incorporating an
image intensifier tube for improved weak-signal gain.
[0023] FIG. 7 is a block diagram of an embodiment incorporating a
fiber-optic distributor and an array of photomultiplier tubes for
improved weak-signal gain.
[0024] FIG. 8 is a block diagram of an alternative embodiment
incorporating multiple spectrometers with an array of
photomultiplier tubes.
[0025] FIG. 9 illustrates an intravascular imaging device according
to an embodiment.
[0026] FIG. 10 is a flowchart illustrating steps for obtaining and
reconstructing images using multispectral methods.
[0027] FIG. 11 is a simulation data set with a localized region of
increased hemoglobin, oxygen saturation, and water.
[0028] FIG. 12 is a reconstruction of phantoms with a single
inclusion where the hemoglobin concentration and the scattering
amplitude are systematically varied.
[0029] FIG. 13 is an experimental setup and graphs of resulting
spectra and first order finite difference spectra resulting
therefrom.
[0030] FIG. 14 shows phantoms with five distinct inclusions.
[0031] FIG. 15 compares images of liquid tissue-simulating phantoms
obtained by Spectral Derivative Image Reconstruction (SDIR) and
Direct Chromophore Spectral Reconstruction (DCSR).
DETAILED DESCRIPTION
[0032] It has been found that the spectral characteristics of
chromophores and scatterers within mammalian tissue are
sufficiently broad that, if visible or near-infrared light from a
group of single-mode lasers is used to interrogate tissue with
small wavelength separation between lasers, radiation from each
laser suffers levels of attenuation and scattering largely similar
to radiation from the other lasers of the group.
[0033] When lasers at separate wavelengths are associated with
different source positions, a spectral encoding of source origin
occurs in a way that can be detected and decoded in parallel at any
reception point.
[0034] FIG. 1 shows a spectral-encoding system 100 for high-rate
hemodynamic tomography. System 100 includes a near infrared
apparatus 110, mammalian tissue 120, an image construction system
130, and an image display 140. A tissue model, or phantom, may be
used in place of tissue 120 during development and calibration.
Near infrared apparatus 110 generates path attenuation data 150
from tissue 120. The image construction system 130 is configured to
receive path attenuation data 150 from near infrared apparatus 110
and construct an image of tissue 120 on image display 140. Image
construction system 130 collects tissue-dependent path attenuation
data 150 from near infrared apparatus 110, determines voxel
attenuation parameters of a tissue model 160, and projects 170 the
voxel attenuation parameters into tomographic image planes to
generate tomographic images for display on image display 140 using
algorithms known in the art. Images are also recorded for later
study.
[0035] FIG. 2 is an abbreviated schematic 200 of the near infrared
apparatus 110 of FIG. 1. In an embodiment, near infrared apparatus
110 includes eight diode lasers 210, a spectrometer 220 and a CCD
230. Spectral encoding of diode lasers is achieved by using a
number of lasers each operating upon a distinct wavelength in the
same spectral band, preferably spaced 0.5 to 1 nm apart within a 4
nm to 10 nm nominal bandwidth. It is anticipated that tighter laser
spacing will be used in future versions of the apparatus,
especially in embodiments having more lasers. Each diode laser of
the eight diode lasers 210 illuminates tissue 120 at laser-specific
locations through transmit optic fibers 212 and tissue distributor
214 simultaneously. Light from each laser of lasers 210 is applied
at a different location along the periphery of tissue.
[0036] Light from lasers 210 penetrates tissue 120. Some of this
light is absorbed in the tissue, some is scattered. Light is
received from tissue 120 through tissue distributor 214 and receive
optic fibers 216 into spectrometer 220. Receive optic fibers 216
are arranged along light entry slit 218 of spectrometer 220, such
that light from each fiber 216 enters at a separate location along
entry slit 218. Light admitted through entry slit 218 passes
through a diffraction grating, and is spread into its spectral
components as it is projected onto a charge-coupled device (CCD)
image sensor 230. Signals from CCD image sensor 230 are transmitted
to image construction system 130 of FIG. 1. Signals from CCD image
sensor 230 encode received light amplitude for each reception point
at each wavelength corresponding to each of lasers 210.
[0037] Light from each receive optic fiber 216 may, and often does,
include light scattered through tissue 120 from more than one of
lasers 210. Since lasers 210 operate on separate wavelengths, the
diffraction grating of spectrometer 220 separates these
wavelengths, such that light received from each laser 210 through
each receive optic fiber 216 illuminates a separate location on CCD
image sensor 230.
[0038] Since light from each receive optic fiber 216 enters
spectrometer 220 at a separate location along entry slit 218, the
CCD image sensor 230 is illuminated with a light pattern similar to
that illustrated in FIG. 3. Light originating at a first laser of
lasers 210(1) and received through a first fiber 216(1) arrives at
location 302. Light received through the first fiber 216(1) but
originating from a second laser of lasers 210(2) arrives at a
second location 304, and light originating from the first laser of
lasers 210(1) and received through a second fiber of fibers 216(2)
arrives at a third location 306 on the CCD image sensor 230. While
this received light pattern is used by image construction system
130 to construct an image of tissue 120, the received light pattern
is not directly an image of tissue 120.
[0039] In an embodiment, CCD image sensor 230 of FIG. 2 has an
array of 512.times.512 separate sensor elements, each capable of
transducing near infrared or visible light into a signal. Signals
from the separate sensor elements correspond to path attenuation
information and are encoded into a CCD output signal for use by
image construction system 130. It is anticipated that the present
apparatus will function with CCD image sensors 230 of other array
sizes, particularly those having a greater number of sensor
elements.
[0040] For monochrome imaging of heme concentration, it is
desirable that each laser of lasers 210 be close, preferably within
one percent, to a center wavelength of a selected wavelength range
so that scattering of each laser is similar to the other lasers. It
is also desirable that the center wavelength be close to 800-810
nm, because at this wavelength heme light absorption is similar for
oxygenated and deoxygenated hemoglobin. Useful images are
obtainable if the center wavelength lies between 620 and 1000
nm.
[0041] In a particular embodiment, each of lasers 210 is within the
wavelength band of 775.0 to 785.0 nm; hence each laser's bandwidth
is less than two percent of its center wavelength, but lasers 210
are spaced apart in that band by approximately 1.2 nm, and each is
within fifty nm of 800-810 nm. In an embodiment, the diode lasers
210 are capable of 50 mW each. The lasers are mounted on
thermoelectric coolers such that laser operating wavelengths are
stable.
[0042] In near infrared apparatus 110 a spatially-varying neutral
density filter is interposed between lasers 210 and transmit fibers
212 to even out the intensity of illumination, compensating for
variations in laser power. Similarly, a spatially-varying neutral
density filter is interposed between receive fibers 216 and slit
218 to compensate for variations in receive fiber coupling from
tissue 120.
[0043] In an alternative embodiment, not shown, there are sixteen
receive fibers 216 instead of the eight previously discussed. It is
anticipated that the present apparatus is operable with, and may
provide improved resolution with, other and greater numbers of
receive fibers.
[0044] It has been found that sufficient data for images of rodent
crania can be acquired in 10 milliseconds while operating the
lasers at 10 mW. It is anticipated that imaging oxygen saturation
and heme concentration of the human brain cortex may require
operation at between 1-100 mW, for 10 to 50 ms. These capture times
can support imaging at video rates.
[0045] Specific applications in which the system could be used
include: imaging blood pulsation in tissue, to assess disease or
response to therapy; monitoring uptake or retention of drugs which
are optically absorbing or scattering; detection of epidural and
subdural hematomas and active intracranial bleeding; imaging of
breast tumors and response of the tumor to external stimuli such as
different breathing gases, applied pressure, and/or vascular flow
changes; imaging fast temporal changes in blood flow in response to
an injected drug, such as monitoring peripheral vascular disease
response to a drug, or tissue ischemia response to a drug; and
uptake and wash out of vascular or tissue maker drugs.
[0046] For imaging of heme oxygenation as well as heme
concentration, it is desirable that there be two groups of
illumination source lasers, a short wavelength group and a long
wavelength group. Lasers of each group should operate at a
wavelength near to a center wavelength of the group. Both groups of
lasers should operate in the 620 to 1000 nanometer band, but the
center wavelengths for the groups should be spaced apart. Spacing
the center wavelengths between five and fifteen percent wavelength
apart will provide resolution of oxygenation.
[0047] In another, dichrome, embodiment as illustrated in FIG. 4,
suitable for imaging oxygen saturation in tissue as well as heme
concentration in tissue, there are eight diode lasers of a first
group 402 having wavelengths spaced in the band from 775 to 780 nm,
spaced approximately 0.5 nm apart, and eight diode lasers of a
second group 404 having wavelengths spaced 0.5 nm apart in the band
of 820 to 825 nm. The center wavelengths of the first group and
second group are therefore separated by approximately six percent,
and each group is less than one percent wide.
[0048] Near infrared light from the diode lasers of both groups
402, 404 is coupled through transmit fibers 406 and tissue
distributor 408 into mammalian tissue under study. Light from the
mammalian tissue under study is coupled from tissue distributor 408
through receive fibers 410 into spectrometer slit 412 and
spectrometer diffraction grating 414. After passage through
diffraction grating 414, the light is projected onto a CCD sensor
array 416. Light originating in the first group of lasers 402
arrives at laser-and-reception fiber specific locations in a first
region 418 of CCD sensor array 416. Light originating in the second
group of lasers 404 arrives at laser-and-reception fiber specific
locations in a second region 420 of CCD sensor array 416. The CCD
sensor array 416 is periodically scanned, pixel exposure
information is digitized, and the digitized data is transferred to
an image construction system 422. In this embodiment, CCD sensor
array 416 is a 2048.times.1024 pixel array. It is anticipated that
filters are provided to reduce sensitivity of the sensor array 416
to stray incident light, such as visible light, outside the band of
interest.
[0049] Image construction system 422 receives digitized data from
CCD sensor array 416 and uses information from the first region 418
of the array to construct an image of heme of a first "color".
Image construction system 422 also uses information from the second
region 420 of CCD sensor array 416 to construct an image of heme of
a second "color". At any one region of mammalian tissue, the ratio
of light absorption by heme in the first band to absorption by heme
in the second band is dependent upon oxygen saturation of heme in
that region of tissue. The first "color" and second "color" images
are therefore compared to produce an image of oxygen saturation in
various portions of the tissue present in the tissue distributor
408.
[0050] In an alternative embodiment, two CCD sensor arrays 416 are
used, one receives light originating in the first group of lasers
402, the second receives light originating in the second group of
lasers 404.
[0051] FIG. 5 illustrates a flowchart 500 of a method for
constructing tomographic images of heme concentrations in tissue.
With reference to FIGS. 1 and 2, near infrared apparatus 110
includes a group of laser light sources 210 for generating 502
infrared light of laser-specific wavelengths in a narrow band of
wavelengths. Light from these lasers 210 is collected and applied
504 to mammalian tissue 120 through transmit fibers 212 and tissue
distributor 214, then light from tissue 120 is received 506 through
tissue distributor 214 and receive fibers 216 at multiple reception
points on tissue 120. Light received 506 from tissue is separated
508 by spectrometer 220 into its component wavelengths, each
wavelength corresponding to a specific laser of laser light sources
210, while maintaining separation according to receive point. This
light is transduced 510 by CCD sensor 230 into electronic signals
corresponding to path attenuation. The electronic signals
corresponding to path attenuation are input to an image
construction system 130, where a tomographic image of the tissue is
constructed 512 as previously described.
[0052] It can be desirable to have high photodetector gain when
imaging structures deep in mammalian tissue. The embodiment
illustrated in FIG. 6 is an alternative embodiment having similar
componentry to what was as previously described with reference to
FIG. 4. In this embodiment, however, received infrared light
transiting the diffraction grating 416 impinges not directly upon
the CCD image sensor 418 of FIG. 4, but upon the photocathode of a
third-generation image intensifier tube 620. Light from the image
intensifier's luminescent anode projects onto a CCD image sensor
622. Data indicative of received intensity at each wavelength and
reception fiber is encoded and transmitted to the image
construction system 624.
[0053] The embodiment illustrated in FIG. 7 is an alternative
embodiment having similar componentry to what was previously
described with reference to FIG. 4. In this embodiment, however,
received infrared light transiting the diffraction grating 416
impinges not directly upon the CCD image sensor 418 of FIG. 4, but
upon the fibers of a fiber-optic coupler and distribution apparatus
722. Fiber-optic coupler and distribution apparatus 722 distributes
light received from each combination of receive fiber 410 and
wavelength through distribution fibers 724 into a separate
photomultiplier tube 726 of photomultiplier tube array 728. In a
monochrome embodiment, photomultiplier tube array 728 is an eight
by eight (64-tube) array, while in a dichromatic embodiment,
photomultiplier tube array 728 is a 128-tube array. Data from the
photomultiplier tube array 728 indicative of received intensity at
each wavelength and reception fiber is encoded and transmitted to
the image construction system 730.
[0054] The embodiment illustrated in FIG. 8 is an alternative
embodiment having short-wavelength lasers 802, long wavelength
lasers 804, transmit fibers 806, and tissue distributor 808 as
previously described with reference to FIG. 4. Receive fibers 810,
however, distribute received light to multiple spectrometers 812,
such that each receive fiber feeds one spectrometer 812. In this
embodiment, received infrared light from at least one receive fiber
enters a slit 816 of each spectrometer 812, then transits the
associated diffraction grating 818. This light is projected upon
fibers of a fiber-optic coupler and distribution apparatus 820 for
each spectrometer 812. Fiber-optic coupler and distribution
apparatus 820 distributes light received as the wavelength
associated with illumination lasers 802, 804 through distribution
fibers 822 into a separate photomultiplier tube 824 of
photomultiplier tube array 828. In a monochrome embodiment,
photomultiplier tube array 828 is an eight tube array for each
spectrometer, in a dichrome embodiment photomultiplier tube array
828 is a sixteen tube array for each spectrometer. Data from the
photomultiplier tube arrays 828 of all eight spectrometers 812,
indicative of received intensity at each wavelength and reception
fiber, is encoded and transmitted to the image construction system
830.
[0055] One embodiment of an optical imaging system includes a novel
intravascular/intraluminal imaging device. The
intravascular/intraluminal imaging device combines aspects of
optical coherence tomography (OCT) and NIR diffuse optical
tomography (NIR-DOT). OCT uses optical echoes of a low coherent
infrared light source (normally around 1300 nm) directed at tissue
to create high-resolution tomographic images. The axial resolution
of OCT is on the order of about 2 to 30 .mu.m depending on the
spectral width of the source, and the lateral resolution of OCT is
on the order of about 5 to 30 .mu.m as determined by the beam
waist. However, in vivo intravascular OCT imaging is generally
deficient in functional information because it measures
beck-reflected coherent light that is strongly influenced by blood
interference and low tissue penetration (.about.1.5 mm). On the
other hand, NIR-DOT between about 400 and 2500 nm identifies the
chemical contents of biological specimens with high contrast, but
is subject to low resolution as a result of diffuse light
detection. This non-linear transport leads to a hypersensitivity to
the boundary, thus small errors in the measurement can
significantly degrade the performance of NIR-DOT by introducing
artifacts within the edge of the reconstructed image.
[0056] The intravascular/intraluminal imaging device described
herein combines the high-resolution structural imaging capability
of OCT with the abundant chemical information of NIR-DOT to provide
both high resolution morphology and high-contrast functional
information. The imaging device includes a catheter-based probe
that may be used for imaging atherosclerotic plaque, endourologic
detection of prostate cancer, analysis of aortic dissection and
numerous other applications. The intravascular/intraluminal imaging
device may further include a wire for indicating the device
location by X-ray radiography, a lumen for delivery of contrast
agents, and/or a balloon for angioplasty. Additionally, the imaging
device may be used in combination with an endoscope.
[0057] FIG. 9 (not to scale) illustrates an
intravascular/intraluminal imaging device 900 that includes a
spectrometer 902, an OCT unit 904, a NIR encoder 906 and a probe
908. Probe 908 includes a housing 909 having an OCT fiber 910
centrally disposed and a plurality of NIR fibers 912 and detector
fibers 914 disposed peripherally around OCT fiber 910. A
cross-section 915 of probe 908 along line A-A shows the fiber optic
geometry. It will be understood that various modifications may be
made to the above-described fiber optic geometry. OCT fiber 910
transmits signals 916 from OCT unit 904 to tissue 918. NIR fibers
912 transmit signals 920 from NIR encoder 906 to tissue 918, and
detector fibers 914 transmit signals (not shown) from tissue 918 to
spectrometer 902. OCT unit 904 provides high-resolution
circumferential cross-sectional imaging by deflecting the probing
light through a window 921 in housing 909 with a micromotor 922
driven 90-degree reflector 924, and NIR encoder 906 provides
co-registered cross-sectional spectroscopic tomography by use of a
novel spread-spectrum encoding-decoding technique and a stationary
light reflector 926. A microprocessor 928 of computer 930 directs
spectrometer 902, OCT unit 904, and NIR encoder 906 and stores data
to memory 932.
[0058] Materials for use in the manufacture of external components
of probe 908 should be biocompatible and approved by appropriate
regulatory bodies, such as the Food and Drug Administration.
Housing 909 may be manufactured, for example, of stainless steel,
titanium and other metals, and polymers such as polyethylene,
polytetrafluoroethylene (PTFE) (e.g., Teflon.RTM.), polyurethane,
Dacron.RTM., polyvinyl chloride, polystyrene and combinations
thereof. Window 921 must be transparent to optical and NIR
electromagnetic energy. Suitable materials for the manufacture of
window 921 include, for example, quartz, polystyrene, polycarbonate
and polypropylene.
[0059] Commercial spectrometers 902, OCT units 904, and NIR
encoders 906 may be used in the construction of
intravascular/intraluminal imaging device 900. For example, a
Nicolet Fourier Transform Infrared Spectrometer produced by Thermo
Electron Corporation of Waltham, Mass. may be used as spectrometer
902 and LightLab of Westford, Mass. manufactures a suitable OCT
unit 904.
[0060] The intravascular/intraluminal imaging device may be used
internally and/or externally in the detection and/or diagnosis of
tumors, cysts, aneurysms, hematomas, and plaques in tissue
including, for example, breast, brain, prostate, ovarian, uterine,
cervical, colon, ureter, urethral, heart, liver, esophageal, skin
and pancreatic.
[0061] In addition to the previously described systems and methods,
the present disclosure provides a DCSR method that utilizes
frequency-domain data, which provides a more complete data set and
gives more accurate images than when a continuous-wave approach is
used. The disclosed DCSR methods may also be utilized in
conjunction with the presently disclosed Spectral Derivative Image
Reconstruction (SDIR) method.
[0062] SDIR is a method for multiwavelength diffuse optical
tomography, where instead of using data from each wavelength
separately or even simultaneously, the difference in data intensity
for multiple wavelength pairs is used. See, Xu, H.; Pogue, B. W.;
Springett, R.; Dehghani, H. "Spectral Derivative Based Image
Reconstruction Provides Inherent Insensitivity to Coupling and
Geometric Errors" Optics Letters, in press, (2005). Errors due to
fiber and tissue coupling or external boundaries have a broadband
offset effect, which is largely wavelength independent. Therefore,
by taking the difference of data collected at two adequately spaced
wavelengths, these common errors largely cancel, with the remaining
signal containing the required chromophore and scatterer
information. By properly choosing wavelength pairs, spatial
resolution may be improved and crosstalk between chromophores of
interest minimized. SDIR may reduce image artifacts in
multispectral diffuse tomography, because coupling and boundary
errors may be effectively removed in the data set of the first
derivative of intensity with respect to wavelength while spectral
information related to chromophores and scattering is retained. A
first order approach to the finite differencing is presented
herein, but the model may be adapted to higher order
approaches.
[0063] A potential advantage of SDIR is that only intensity
measurement is required at each wavelength so that the hardware
implementation can be a continuous-wave laser system or a
steady-state broadband system without using a complex frequency or
time domain system that provides secondary measurement to an
optical pathlength. It will be appreciated, however, that
frequency-domain methods may also benefit from SDIR.
[0064] The first derivative or finite difference approach of SDIR
can be used with multiple imaging algorithms (e.g., both continuous
wave and frequency-domain DCSR) on data collected using various
instruments in the diagnosis and/or treatment of a wide variety of
diseases. For example, SDIR may be used to generate images useful
in the diagnosis and/or treatment of breast cancer, brain cancer,
prostate cancer, aneurysms, hematomas, tumors, cysts, heart
disease, renal artery stenosis, peripheral vascular disease, and
vulnerable plaques. Imaging may be performed prior to and/or during
an invasive procedure.
[0065] FIG. 10 is a flowchart illustrating steps for obtaining and
reconstructing images using multispectral methods. The DCSR method
involves obtaining 1002 frequency-domain data from a detector
indicative of light intensity. Using 1004 the logarithm of the
light intensity for multiple wavelengths at one or more
source-detector pairs in a spectrally-constrained algorithm to
reconstruct structural and functional data of the tissue, and
creating 1006 a tomographic image of the tissue. When the SDIR
method is used in conjunction with the DCSR method, light intensity
data is obtained 1008 from the detector, the difference in the
logarithm of the intensity for multiple wavelengths at one or more
source-detector pairs is determined 1010, the difference data is
used 1012 to reconstruct structural and functional data of the
tissue, and a tomographic image of the tissue is created 1006. For
detection of emission sources, no excitation light source is
required. Emitted light is detected by the detector and light
intensity data is obtained 1014 from the detector. The logarithm of
the light intensity for multiple wavelengths at one or more
source-detector pairs is used 1016 to reconstruct emission source
concentration. The emission source concentration data are then used
to create 1006 a tomographic image of the tissue. In all cases,
concentrations are determined by Beer's Law:
A=.epsilon.bc
where A is absorption, .epsilon. is the molar absorbtivity
coefficient (M.sup.-1 cm.sup.-1), b is the pathlength measured in
centimeters, and c is the concentration in units of molarity.
Example 1
Intravascular Imaging Device Construction
[0066] An intravascular imaging device, according to an embodiment,
includes spectrometer 902, OCT unit 904, NIR encoder 906, and
combined OCT/NIR tomographic spectroscopy probe 908. Probe 908 is
about 2 mm in diameter and includes a central OCT fiber 910 and a
plurality of NIR imaging fibers 912 and detector fibers 914
distributed around the periphery of OCT fiber 910. OCT unit 904 is
a conventional time-domain configuration, and the intravascular
circumferential cross-sectional imaging is achieved by the rotation
of a micromotor 922 driven 90-degree reflector 924 that eliminates
axial rotation of probe 908. An imaging frame rate of 4 Hz is used.
NIR encoder 906 incorporates a spread-spectrum encoding-decoding
technique. Diffuse tomographic imaging requires information from
multiple source-detector pairs, and current approaches of decoding
the multiple source-detector pairs either limit the imaging speed
or limit the dynamic range of the detection. In spread-spectrum
encoding, the collimated emission of a high-power low-coherence
source (such as LEDs with spectral-width of about 30 nm and power
of several hundred milliwatts) is dispersed by a diffraction
grating and collimated to a linear fiber bundle 912. Light sources
with different center wavelengths can be dispersed by the same
grating and focused to the same linear fiber bundle 912. The linear
fiber bundle 912 delivers the spread-spectrum encoded light to
probe 908, where the fiber bundle is arranged in a circular
geometry (see cross-section 915). The lights from the circularly
distributed fiber array 912 are reflected by a circular 90-degree
reflector 926 to the vessel wall. The diffused photons are
reflected by the same circular 90 degree reflector 926 and detected
by the detector fiber array 914 that is also displaced in a
circular geometry and co-centric to the NIR fiber array 912 (see
cross-section 915). The detected light is then delivered to
spectrometer 902 where the detector fiber bundle 914 becomes linear
again, and signals 920 from spread-spectrum-encoded sources are
separated for parallel decoding and detection from all
source-detector pairs. With this technique, 16 source fibers of 100
.mu.m and 16 detector fibers of 200 .mu.m can be arranged inside
the 2 mm catheter probe 908, resulting in a total of 256 pairs of
optodes (i.e., source-detector pairs).
[0067] Four LEDs, having 30 nm bandwidth each, are incorporated
into the presently described design with center wavelengths of 700
nm, 760 nm, 850 nm and 940 nm. Spectrometer 902 is able to resolve
a 300 nm total range; therefore, the signals from each source cover
about 1/10 of the total range. For a CCD detector of 512.times.512
pixels, 1/10 of the CCD width, which is 50 pixels, is sufficient to
decode the signals from 16 source fibers.
[0068] The tomographic reconstruction of the NIR spectroscopic data
is performed based on a Monte-Carlo model that deals with very
short source-detector distances and superficial photon migrations.
Since the size of the intravascular probe is 2 mm, the longest
distance between source and detector along the circumference is
about 6 mm, therefore, a NIR imaging depth of 3 mm is achieved.
This is about twice as much as that of OCT. In terms of the image
resolution of NIR tomography, 1.times.1 mm.sup.2 produces about 50
pixels in the reconstructed image.
Example 2
Intravascular Imaging Method
[0069] An intravascular imaging device may be used to detect
atherosclerotic cardiovascular disease by measuring the extent of
plaque or measuring the extent and depth of ischemia in a
catheterization procedure. The patient receives a mild sedative
such as Midazolam approximately 30 minutes before the procedure.
The groin, neck or forearm is cleansed with a sterilizing solution,
shaved, and covered with a sterile drape. A local anesthetic is
used to numb the area before a small incision is made and a sheath
is inserted into the artery (e.g., femoral artery, carotid artery)
or vein. The probe of the intravascular imaging device is passed
through the sheath and threaded to the aorta, coronary artery
and/or left ventricle of the heart. During probe insertion, probe
location is monitored using an X-ray machine that produces
real-time images (fluoroscopy) and a radio-opaque wire within the
probe.
[0070] Once the probe is in place, OCT and NIR signals at multiple
wavelengths are sent sequentially to the probe. The return signals
are sent to the spectrometer and results are saved in a computer
memory. A second and subsequent set of data is collected by moving
the probe a short distance and repeating the illumination/detection
protocol. A microprocessor manipulates the raw data using SDIR
algorithms and creates a three-dimensional tomographic image that
may be viewed in real time on a display.
Example 3
Direct Chromophore Spectral Reconstruction and Spectral Derivative
Image Reconstruction
[0071] The general diffuse optical tomography (DOT) reconstruction
algorithm is based on a standard least squares error optimization,
where the recovery of .mu..sub.a and .mu.'.sub.s distribution is
based on measurements of light fluence at the tissue surface. The
inverse solution is achieved by minimizing the difference between
measured (observed) fluence .PHI..sup.o at the tissue surface and
calculated data .PHI..sup.o from a given model. This is a
minimization of .PSI.:
.PSI.=.parallel..PHI..sub..lamda..sub.1.sup.c(x)-.PHI..sub..lamda..sub.1-
.sup.o.parallel..sub.2.sup.2 (1)
Where column vectors .PHI..sub..lamda..sub.1.sup.c and
.PHI..sub..lamda..sub.1.sup.o represent calculated and measure
fluence at all the source-detector pairs at wavelength
.lamda..sub.1. Vector x is the unknown parameter and is the
spatially distributed optical properties,
x=[.mu..sub.a(.lamda..sub.1).mu.'.sub.s(.lamda..sub.1)]. Equation 1
can be reformulated into a method to recover the chromophore,
fluorophore and/or bioluminescent source data directly by use of
multiple wavelengths of emission data. This is called the Direct
Chromophore Spectral Reconstruction (DCSR) method. The DCSR method
is based on two principles. First, that the wavelength dependent
absorption is a linear combination of absorbing components in the
tissue,
.mu..sub.a(.lamda..sub.1)=.SIGMA..sub.i.epsilon..sub.i(.lamda.)c.sub.i,
where .epsilon..sub.i is the specific extinction coefficient and
c.sub.i is the concentration map of i.sup.th chromophore. Second,
the wavelength dependence of reduced scattering is modeled by an
empirical approximation,
.mu.'.sub.s(.lamda.)=a(.lamda./.lamda..sub.0).sup.-b, where
.lamda..sub.o is a normalization wavelength. A useful setting is
.lamda..sub.o=1 .mu.m, when near-infrared wavelengths are used. The
unknown parameters x=[a b c.sub.1 c.sub.2 c.sub.3] are independent
of .lamda. and therefore the measurement at multiple wavelengths
can be coupled to yield a new objective function:
.PSI. 1 = .DELTA..PHI. c - o 2 2 = { .PHI. .lamda. 1 c ( x ) .PHI.
.lamda. 2 c ( x ) .PHI. .lamda. m c ( x ) } - { .PHI. .lamda. 1 o
.PHI. .lamda. 2 o .PHI. .lamda. m o } 2 2 ( 2 ) ##EQU00001##
Image reconstruction with the objective function in Equation 2 is a
useful way to directly recover chromophore concentrations, and
constrain the concentration values in a way which fits with the
known extinction coefficient spectra of the constituents of the
tissue.
[0072] The iterative formula for converging to a solution can be
derived as .DELTA.x=(I.sup.TI).sup.-1I.sup.T.DELTA..PHI..sup.c-o,
where I is the Jacobian matrix comprising the sensitivity for each
parameter and the measurement at each wavelength:
= [ a b c 1 c 2 c 3 ] = [ a , .lamda. 1 b , .lamda. 1 c 1 , .lamda.
1 c 2 , .lamda. 1 c 3 , .lamda. 1 a , .lamda. 2 b , .lamda. 2 c 1 ,
.lamda. 1 c 2 , .lamda. 2 c 3 , .lamda. 2 a , .lamda. m b , .lamda.
m c 1 , .lamda. m c 2 , .lamda. m c 3 .lamda. m ] ( 3 )
##EQU00002##
[0073] Alternatively, instead of using each wavelength of emission
data as is, a spectral derivative data set can be created from the
difference in logarithm of intensity emitted at each wavelength.
This is called the Spectral Derivative Image Reconstruction
(SDIR).
[0074] In the SDIR approach, the objective function is modified
from Equation 2:
.PSI. 2 = .DELTA..PHI. 'c - o 2 2 = { .PHI. .lamda. 1 c ( x ) -
.PHI. .lamda. 2 c ( x ) .PHI. .lamda. 2 c ( x ) - .PHI. .lamda. 3 c
( x ) .PHI. .lamda. m - 1 c ( x ) - .PHI. .lamda. m c ( x ) } - {
.PHI. .lamda. 1 o - .PHI. .lamda. 2 o .PHI. .lamda. 2 o - .PHI.
.lamda. 3 o .PHI. .lamda. m - 1 o - .PHI. .lamda. m o } 2 2 ( 4 )
##EQU00003##
Here the prime denotes the finite difference operator to remind one
of the similarity between derivative and finite difference. The
Jacobian matrix for SDIR can be derived from the Jacobian
calculated using the conventional method and is the subtraction of
the first m-1 row and the last m-1 row of I in Equation 3. The
sequence of pairs of wavelengths used in Equation 4 can be
arbitrary, for example one can use {[.lamda..sub.1, .lamda..sub.2],
[.lamda..sub.1, .lamda..sub.3], . . . } or {[.lamda..sub.1,
.lamda..sub.2][.lamda..sub.2, .lamda..sub.3], . . . }
[0075] Equation 4 is useful, for example, where coupling
coefficient error exists in the measurement so that measured
intensity I.sup.o is the product of the coupling efficiency k and
the real intensity I.sup.r, I.sup.o=kI.sup.r, since typically in
the model .PHI..sup.o=log(I.sup.o)=log(I.sup.r)+log(k), the
coupling error becomes an additive term. Using this measurement
with coupling error, the SDIR algorithm (Equation 4) will not be
affected, but Equations 1 and 2 treat the coupling error as part of
the real signal and will lead to image artifacts.
[0076] The Jacobian matrix is a function of the wavelength pairs
chosen. If wavelength pairs are chosen where higher absorbance
contrast can be achieved for chromophores, more sensitive and
independent sub-Jacobian matrices (I.sub.a, I.sub.b, I.sub.c1,
I.sub.c2, I.sub.c3) can be constructed to provide better separation
and localization of chromophore concentrations.
[0077] Computational analysis of the algorithms described above is
performed by image construction system 130, 422, 624, 730.
Example 4
Multispectral Image Reconstruction of Hemoglobin, Oxygen Saturation
and Water Fraction in Tissue
[0078] In the multispectral DCSR approach to image reconstruction,
the possible spectral shapes of the chromophore and scattering
models are implemented into the image formation process, thereby
adding a spectral constraint into the reconstruction. This type of
reconstruction uses multiwavelength measurements simultaneously to
estimate images of oxyhemoglobin, deoxyhemoglobin, water and
scatter parameters directly, without intermediate recovery of
optical properties. Assuming the main absorbers in the tissue are
oxyhemoglobin (HbO.sub.2), deoxyhemoglobin (Hb) and water and
knowing their molar absorption spectra (absorption per unit
concentration) at the observed wavelengths, it is possible to
calculate each of their contributions to the absorption at various
points in the tissue. Images representing a map of HbO.sub.2, Hb,
and the ratio of HbO.sub.2 to Hb contributions to absorption are
presented to a user of the system to permit diagnosis.
[0079] Relationships from Equations 3 and 4 form the basis of the
spectral approach to image reconstruction, which involves direct
recovery of images of the concentrations of HbO.sub.2, Hb and water
and scatter amplitude and power by coupling multiwavelength
measurements together. This approach also uses Newton's method
along with the Levenberg-Marquardt regularization, but the
minimization includes the measurements from all observed
wavelengths. The least squares function is rewritten as
.chi. 2 = j = 1 Mn ( .phi. j m - .phi. j c ) 2 , ##EQU00004##
so that the sum includes all wavelength measurements (Mn), where n
is the number of wavelengths available. The relationship for each
wavelength, with the spectral constraints included, is represented
by
.differential..phi..sub..lamda.=I.sub.c,.lamda.dc+I.sub.A,.lamda.dA+I.su-
b.b,.lamda.db, (5)
where I.sub.c,.lamda., I.sub.A,.lamda. and I.sub.b,.lamda.
represent the Jacobians for each of the chromophore and scattering
parameters and the update occurs in terms of the chromophores,
.differential.c, directly:
({tilde over (I)}.sup.T{tilde over
(I)}+.alpha.I).differential.c={tilde over
(I)}.sup.T.differential..phi., (6)
where
.differential..phi.=(.phi..sup.m,.lamda.-.phi..sub.k.sup.c,.lamda.)-
.sub..lamda.=1:n and {tilde over
(I)}=[I.sub.c,.lamda.,I.sub.A,.lamda.,I.sub.b,.lamda.].sub..lamda.=1:n
and .alpha. is the regularization parameter. The technique reduces
the total number of unknown parameters in the image reconstruction
(from number of wavelengths times optical properties to five
parameters overall) and makes the inverse problem better posed by
increasing the stability to noise in the measured data. The
technique is optimized in terms of obtaining initial estimates of
the parameters, regularization, convergence criteria, filtering and
to allow for the best calibration procedure for the data. It has
been validated in homogeneous imaging fields, simulations and
experiments. The results indicate that higher qualitative and
quantitative accuracy, as well as reduced crosstalk between the
functional parameters, is achieved.
[0080] The spectrally constrained approach is inherently robust due
to the addition of a priori spectral behavior, and requires less
spatial filtering. In studies using simulated and experimental
data, at 1% noise, which is a typical level found in tomography
systems, the reduction in standard deviations in oxygen saturation,
water and scatter power were significant. The trend was continued
at 5% noise in the amplitude and phase data (5% is near the limit
of data noise found in typical measurement systems). The spectrally
constrained technique yields quantification accurate to within 15%
of true values, whereas using the traditional method, high standard
deviations make it impossible to obtain useful NIR information.
There is a significant reduction in the crosstalk between
oxyhemoglobin and water, due to prior knowledge of the spectral
shapes, as well as with deoxyhemoglobin and scatter parameters.
These constraints are also responsible for the robust nature of the
method to higher levels of noise as compared to traditional
methods.
[0081] FIG. 11 shows a simulation data set with a localized region
of increased hemoglobin, oxygen saturation, and water in a region
left of center, with homogeneous scattering amplitude and
scattering power. The tissue phantoms are shown in the top row of
images. Images resulting from traditional reconstruction methods
are shown in the bottom row. To obtain the traditional images,
transmission data at six near-infrared wavelengths was generated
with an average of 1% noise in amplitude. The data were used to
recover images of the chromophores and scatterers. Reconstruction
of the six wavelengths was performed to determine absorption and
scattering coefficients separately and then the data were fit for
the values of Hb, S.sub.tO.sub.2, water and scattering amplitude
and power. Using the DCSR approach, where all wavelengths are
reconstructed together, the images shown in the middle row
resulted. The DCSR approach provided fewer image artifacts and more
accurate values of each parameter.
[0082] FIG. 12 shows reconstructions of phantoms with a single
inclusion where the hemoglobin concentration and the scattering
amplitude were systematically varied. Increasing hemoglobin
concentration is shown in the upper series of images and increasing
scattering amplitude is shown in the lower series of images. Again,
a representation of the true phantom images is shown in the top row
of each series. The DCSR images are shown in the middle row of each
series, and the traditional, separate wavelength-approach images
are shown in the bottom row of each series. These images of
experimental data at different contrasts validates that the DCSR
approach provides images which are closer to the true expected
values.
Example 5
Spectral Derivative Image Reconstruction of Hemoglobin
[0083] The SDIR method may overcome several inherent measurement
errors such as coupling coefficient variation, boundary reflection
mismatch and geometric mismodeling. The difference or derivative
spectrum is used to cancel the common error term seen at each
wavelength while maintaining the scattering and chromophore
spectral and spatial contrast. SDIR may be adapted to various
imaging modalities where multispectral information is
available.
[0084] FIG. 13 shows an experimental setup and graphs of resulting
spectra and first order finite difference spectra resulting from
the illustrated setup. Experimental setup 1300 shows positions of a
source S and detectors D1, D2, where source-detector pairs, e.g.,
S-D1 and S-D2, may be formed by the combination of one source and
one detector. It will be appreciated that multiple sources may be
present (S1, S2 . . . Sn) and that source-detector pairs may also
be formed by substituting one source for another, e.g., S1-D1,
S2-D2. The illustrated geometry was used to measure a homogeneous
diffuse blood phantom using a broadband NIR tomography system.
Graph 1302 shows measured attenuation spectra at D1 and D2, and
their difference (D1-D2). Ideally, the spectra of the symmetric
detectors for a symmetric homogeneous phantom should overlap
precisely, but in reality small differences are seen due to the
different coupling coefficient as a function of the contact fibers
on the phantom surface. Graph 1304 shows the first order finite
difference spectra of D1 and D2 at two wavelengths that are
separated by 20 nm. As shown, finite difference spectra display
less error than spectra from identically situated detectors. This
experiment confirms that SDIR can remove artifacts related to
coupling coefficients.
[0085] FIG. 14 shows phantoms 1400 with five distinct inclusions
1402. The five inclusions 1402 are denoted in the column headings
as scattering amplitude, a; scattering power, b; deoxyhemoglobin
concentration, [H.sub.bR]; oxyhemoglobin concentration,
[H.sub.bO.sub.2]; and water concentration, [H.sub.2O]. Phantoms
1400 for this experiment have a total diameter of 27 mm and
background and inclusion parameters as shown in Table 1:
TABLE-US-00001 TABLE 1 Parameters for background medium and
inclusions of FIG. 14. [H.sub.bR] [H.sub.bO.sub.2] a(10.sup.-3b
mm.sup.b-1) b (.mu.M) (.mu.M) [H.sub.2O] Background 1.0 1.4 0.01
0.01 0.5 Inclusions 1.5 1.0 0.02 0.02 1
Data measured at 13 wavelengths from 670 nm to 910 nm, in 20 nm
increments, were simulated for a linear triangular mesh with 425
nodes, having an equally distributed set of 8 sources and 8
detectors around the boundary. Only intensity measurement was
considered. A half percent of Gaussian random noise was added to
all synthesized data. The lower row illustrates phantoms 1400 where
5% Gaussian coupling coefficient error was added to the data shown
in the upper row.
[0086] FIG. 15 compares images of liquid tissue-simulating phantoms
1400 (FIG. 14) obtained by Spectral Derivative Image Reconstruction
(SDIR) and Direct Chromophore Spectral Reconstruction (DCSR). The
left hand block of images (corresponding to bracket 1500) shows
SDIR images; the right hand block of images (corresponding to
bracket 1502) shows DCSR images. Row A shows images with no data
error; Row B has 5% randomly distributed data error; Row C includes
boundary reflection coefficient modeling errors; and Row D
reconstructs data taken from a distorted boundary shape. Normalized
RMS error of each reconstructed image is shown in Table 2, where
all values shown are percentages.
TABLE-US-00002 TABLE 2 Comparison of normalized RMS error between
SDIR and DCSR images. SDIR DCSR Row a b [H.sub.bR] [H.sub.bO.sub.2]
[H.sub.2O] a B [H.sub.bR] [H.sub.bO.sub.2] [H.sub.2O] A 11.2 11.1
15.2 17.3 18.4 8.2 10.3 13.0 19.4 17.8 B 11.2 11.1 15.2 17.3 18.4
31.5 16.0 16.1 22.2 21.3 C 17.2 7.1 15.1 14.3 16.5 23.5 18.3 21.5
29.1 26.0 D 12.6 7.4 14.8 17.0 16.6 23.5 15.6 90.9 527.4 22.2
Columns a, b, [H.sub.bR], [H.sub.bO.sub.2], and [H.sub.2O]
correspond to the labeled columns in FIGS. 14 and 15. In this
simulation, the SDIR model shows higher tolerance than the DCSR
model to errors in absolute intensity measurement, and reduces
reconstructed artifacts. SDIR also reduces parameter crosstalk and
improves quantitative accuracy, giving increased sensitivity for
oxyhemoglobin within a differential spectrum.
Example 6
Multispectral Bioluminescence Tomography
[0087] Tissue may be treated with a medical imaging composition,
such as luciferase, that causes light emission at one or more
points in the tissue. Measurements of the spectrum of the light
intensity at the tissue surface are recorded using a spectrometer.
These multispectral measurements can be used with the DCSR method
as well as with the SDIR method to reconstruct the size, location
and intensity of the bioluminescence source within the volume. The
emitted light is attenuated differently at each wavelength, and by
incorporating a reconstruction approach that uses all wavelengths
together, there is an improved ability to accurately reconstruct
the source strength and distribution.
[0088] Emitted light from firefly luciferase is a widely
distributed band of wavelengths from 500 nm up to above 650 nm.
When not attenuated, it has a peak emission near 560 nm, but when
detected from within an animal appears to have a peak near 600 nm
with measurable emissions of up to 50 nm above and below this peak.
It is possible to measure the emission at the surface of the tissue
in discrete steps of, for example, 10 nm ranging from between 550
nm and 650 nm, although strong optical absorption at the lower
wavelengths may hinder accurate measurements with adequate signal
to noise.
[0089] The data can be represented by an operator, which is linear
in terms of the bioluminescence source. For simplicity, it is
assumed that absorption and scatter parameters are known; they can
be calculated using NIR data. The image reconstruction method is
posed as a solution to the following minimization expression:
.chi..sup.2=(y-F(B)).sup.2 (7)
where y is the measured data, F is the forward model calculated
with bioluminescence source B(r). A practical approach to solving
this problem is generally developed by assuming that the solution
to this equation can be computed assuming a linear model by
creating a set of independent basis solutions for the source,
B = i = 1 N a i b i , ##EQU00005##
where the coefficients a.sub.i are the weight functions for
multiple sources b.sub.i at all nodes i in the model containing a
total number of nodes in the image. This can be represented in
matrix form as, B=b A, where b is a matrix of size N.times.N and A
is a vector of length N. Each column of matrix b is a unit source
point at each appropriate location, and each element of A
represents the strength (or weight) of that source. The size of b
is reduced, if a coarser basis is used for the source than that of
the diffusion model. Solving this matrix equation for a, in a least
square manner, results in a single step linear expression,
a=W.sup.T(WW.sup.T+.lamda.I).sup.-1y (8)
where W is a matrix containing the solution of the diffusion
equation for all possible source positions N and y is the measured
boundary flux. Here .lamda. is a regularization parameter and I is
the identity matrix. Although the Hessian matrix WW.sup.T is well
conditioned and invertible, the use of .lamda. becomes necessary
with the presence of noise in the data, allowing the damping of
noise within reconstructed images. Herein, .lamda.=0.001% of the
maximum of the diagonal of the Hessian.
[0090] Reconstructions of a two-dimensional circle of radius 20 mm
were modeled as containing 20 .mu.M total blood with 75% oxygen
saturation and 60% water content. The scatter amplitude and power
of the medium were assumed to both be equal to 1. The corresponding
optical properties for a range of wavelengths, 600-650 nm, were
used as for a small animal model, and were used to generate
boundary data, for a single bioluminescence source of 5 mm
diameter. A typical reconstructed image for only one individual
wavelength results in a superficial blur.
[0091] The method of multiwavelength spectral bioluminescent
tomography is such that instead of considering only data from a
single wavelength, multiple data sets that are measured from the
same domain containing the same bioluminescence distribution, over
a range of usable wavelengths should be coupled and used. In this
manner, a={tilde over (W)}.sup.T({tilde over (W)}{tilde over
(W)}.sup.T+.lamda.I).sup.-1{tilde over (y)}, where {tilde over
(W)}=[W.sub..lamda.1; W.sub..lamda.2;W.sub..lamda.3; . . . ;
W.sub..lamda.n] is the weight matrix of all n number of wavelengths
cascaded and {tilde over
(y)}=[y.sub..lamda.1;y.sub..lamda.2;y.sub..lamda.3; . . .
;y.sub..lamda.n] is the measured boundary data of all n number of
wavelengths cascaded. The solution a will be a vector corresponding
to the number of unknowns.
[0092] Images are reconstructed using a combination of multiple
wavelengths. The use of only 2 sets of wavelengths dramatically
improves the qualitative accuracy of the reconstructed image. The
use of additional data sets ranging to 6 wavelength bands improves
both the quantitative and qualitative accuracy of the reconstructed
images. The location of the reconstructed anomaly when 6 wavelength
bands are used is within 1 mm of the original target location.
[0093] To explore the linearity of the reconstructed
bioluminescence versus the true strengths, a set of boundary data
were simulated with varying relative bioluminescence strengths
ranging from 1 to 80. Images were reconstructed using 6 sets of
coupled multiwavelength data ranging from 600-650 nm and the
maximum reconstructed value of bioluminescence versus actual value
was plotted. There was a good linear correlation between the actual
and reconstructed bioluminescence value. Thus, physiological or
functional changes of biological tissue as a function of time may
be accurately determined.
[0094] The above-described method may be utilized to quantify any
emission source present in tissue. Such emission sources may, for
example, be luminescent, fluorescent, and/or phosphorescent.
[0095] The changes described above, and others, may be made in the
systems and methods described herein without departing from the
scope hereof. It should thus be noted that the matter contained in
the above description or shown in the accompanying drawings should
be interpreted as illustrative and not in a limiting sense. The
following claims are intended to cover all generic and specific
features described herein, as well as all statements of the scope
of the present method and system, which, as a matter of language,
might be said to fall there between.
[0096] This specification contains numerous citations to references
such as patents, patent applications, and publications. Each is
hereby incorporated by reference.
* * * * *